Corporate innovation in the AI era is not about attaching an impressive name to a new business. More precisely, nothing changes just because a company says, “We should do AI too.”
In an SBS “Please Take Care of Liberal Arts” video, former KT vice president Sujeong Shin gives a realistic diagnosis of why companies collapse. Old companies do not collapse only because they fail to find new businesses. They collapse when they fail to reread the meaning of their existing business and when internal rules become larger than customers.
This article summarizes what companies must change to grow again in the era of AI transformation.
## If you do not prepare the next S-curve, even strong companies stop
A business usually follows an S-curve. It starts slowly, grows rapidly at some point, enters maturity, and eventually declines.
The problem is that many companies try to survive maturity and decline with the methods created during the growth phase. Past success feels familiar and safe. But that familiarity blocks the next growth.
Companies must therefore always prepare the next S-curve. This does not mean abandoning the existing business and doing something completely unfamiliar. The starting point is to reinterpret the existing business.
## New business is not abandoning the old business; it is reinterpreting it
A striking point in the video is the view of new business. When an existing business becomes difficult, many companies search for something entirely different. Meanwhile, someone else reinterprets gaps in the existing market.
While telecom companies did not fully reread the essence of text messaging and communication, Kakao grew messenger services. While financial companies kept transfers and investments heavy, Toss created a lighter, easier financial experience.
Microsoft is similar. Old Microsoft was closer to a PC software company. After Satya Nadella, the company redefined itself as a company that improves enterprise productivity. Then word processors, cloud, collaboration tools, and AI all connected in one direction.
Walmart also reinterpreted itself not simply as an offline retailer but as a life platform closest to customers. It expanded from a place that sells goods into logistics, daily-life services, and a data-based retail platform.
The point is simple: new business does not start by looking somewhere random. It starts by asking again what the essence of the work you already do is.
## Every company must now become an AI company and a technology company
In the AI era, “we are a traditional industry, so AI is far from us” is becoming less persuasive. Manufacturing, shipbuilding, retail, cosmetics, education, and logistics are no exceptions. The key is not to view AI separately, but to combine it with the existing business.
Companies with existing industries may actually have an advantage because they already have data, customer touchpoints, field experience, and physical assets. AI does not create business in the air. It becomes powerful when connected to real problems.
Shipbuilding can attach AI to design, maintenance, safety, and process optimization. Retail can attach AI to demand forecasting, logistics, and personalized recommendations. Cosmetics can attach AI to skin data, preference analysis, and product-development speed.
The question of AI transformation is not “Should we create a new AI business?” Better questions are:
– What is the essence of our business?
– Where do customers actually feel inconvenience?
– Can AI and technology solve that inconvenience faster and more accurately?
– Are our existing organizational processes blocking that change?
## Zero-to-one takes time, which is why large companies struggle to endure it
New businesses pass through two broad stages. The first is zero-to-one: finding the product or service customers truly want. The second is one-to-ten: scaling the model already found.
One-to-ten is close to operations and management. Zero-to-one is different. There is no right answer, and timing and luck matter. It requires repeated attempts, discards, and rebuilds.
That is why zero-to-one often fits startups better. Startups can try quickly and pivot when they fail. Large companies are slower and often cannot wait long for small results.
Early revenue from a new business is small. In a company with a one-trillion-won core business, a 100-million-won experiment looks shabby. But if the company cannot endure that small sprout, the next business cannot grow.
This is why an ecosystem in which large companies invest in startups, then acquire or partner with them when growth becomes visible, is important.
## Startups should be awls, not hammers
If a startup fights a large company head-on, it is at a disadvantage in capital, people, brand, and distribution. Early startups should therefore be awls, not hammers.
The awl strategy means digging into a small but sharp market. Start in a niche that large companies do not care about, understand customers deeply there, create loyal customers, and then expand sideways.
Toss did not start as a giant comprehensive financial platform. It started with the small, specific inconvenience of simple money transfers. Coupang also did not dominate all retail from the beginning; it obsessively improved customer experience and created lock-in.
Early startups should ask not “How large a market can we claim?” but:
– What small, sharp problem can we solve best?
– What customer pain are large companies not yet taking seriously?
– If we solve this problem, will customers have a reason to stay?
– Can we become number one in this narrow area?
## Bureaucracy is not only bad, but it becomes dangerous when hardened
As companies grow, some bureaucracy naturally appears. Responsibility increases and risk management becomes necessary. Approval procedures and systems are needed. When there are many customers, roughly moving fast can be dangerous.
The problem begins when bureaucracy swallows the organization’s purpose. Reports become more important than customers, and approval lines become more important than the field. Members say “the rule does not allow it” before judging for customers.
Three things are needed to revive such an organization.
### 1. Make the sense of crisis clear
Organizations do not change unless they feel real danger. Repeating “Let’s innovate” is not enough. People must share the reality that the current way may lose customers, lose markets, and eventually shake jobs.
### 2. Go back down to customers and the field
Desk strategy alone cannot revive an organization. Executives and leaders must meet customers, listen to field problems, and directly confirm what customers are tolerating and why they leave.
### 3. People who innovate must actually be recognized
Organizational culture is not a poster slogan; it is a way of survival. Members watch rewards more than words. If people who try innovation are pushed out after failure while people who protect old methods are promoted, nobody believes in innovation.
To change for real, the signal that people who execute innovation are recognized and promoted must appear repeatedly. It must become a steady reward system, not a short-term event.
## Systems must work with mission, not become 100% of the organization
Growing companies need systems. As people and work become complex, standards and processes are necessary. Without systems, quality and responsibility become unclear.
But when systems become too large, people forget the essence of the work. Marketers see only marketing systems; HR people see only HR rules. Following internal procedures becomes bigger than understanding customers’ inconvenience.
The video mentions Disney. Disney is a highly systemized organization, but it leaves room to move by mission. The purpose of delighting and satisfying customers enables judgment beyond written rules.
Not every company can use the same ratio. Aviation, manufacturing, and healthcare require more system because safety is crucial. Content, IT services, and software can allow more room for experimentation.
The important thing is balance between system and mission. Systems make work stable; mission restores customer context that systems miss.
## Do not copy success cases; learn failure conditions
Companies love success cases: Netflix’s “no rules,” Silicon Valley autonomy, famous HR systems, and specific CEOs’ leadership.
But success formulas are not universal. Some methods work only in a specific industry, time, talent density, or founder philosophy. If you import the system without the context, side effects appear.
When studying success, ask not “Should we do the same?” but “Under what conditions did it work?” More importantly, learn failure conditions.
Accounting fraud, ignoring customer churn, internal-rule-first thinking, a culture that kills small experiments, and innovation rewards that exist only in words clearly damage organizations. Success is hard to copy, but the probability of failure can be reduced.
## Five questions for corporate innovation in the AI era
To check whether an organization is really changing, ask:
– How are we redefining our existing business?
– Are AI and technology actually connected to solving customer problems?
– Do we have a structure that can endure small results from new businesses for at least three years?
– Does the voice of customers and the field reach decision-makers directly?
– Are people who execute innovation, not merely talk about it, being recognized?
If these questions cannot be answered, AI transformation is likely to remain a slogan.
## Conclusion: innovation is not a new business name but a change in survival style
Corporate innovation in the AI era does not end with a list of technologies to adopt. The more fundamental question is: what makes our company meaningful to customers, and how will we remake that meaning amid today’s technology and market changes?
Companies rarely collapse because they do not know change is coming. They collapse because they know but cannot change. When rules, reporting, approvals, and past success become larger than customers, organizations slowly harden.
Companies that grow again are different. They reinterpret existing businesses, attach AI and technology to customer problems, endure small experiments, return to the field, and actually reward people who innovate.
Ultimately, corporate culture is not words but a way of survival. That principle does not change in the AI era.
## Further reading
– [Anthropic Mythos Shock: As AI Becomes a Strategic Asset, What Should Korea Prepare?](https://www.thinknote.co.kr/anthropic-mythos-ai-strategic-asset-korea/)
– [AI Agent Evolution: What OpenClaw Shows About the Next Step Beyond Chatbots](https://www.thinknote.co.kr/ai-agent-evolution-openclaw-action-oriented-ai/)
– [Innovative Small Business AI Support: Eligibility, Scale, and Checklist](https://www.thinknote.co.kr/innovative-small-business-ai-support-2026/)
– [AI-Native Workflows: How to Rebuild Work Around a Digital Brain and AI Agents](https://www.thinknote.co.kr/ai-native-workflows-digital-brain-ai-agents/)
## References
– Original video: [“We say innovation, but nothing actually changes” — SBS](https://www.youtube.com/watch?v=RfmKYC1t-hE)
## FAQ
### What is the starting point for corporate innovation in the AI era?
It is not abandoning the existing business, but redefining its essence. Then AI and technology must be connected to solving customer problems.
### Why do large companies often fail at new businesses?
They cannot wait long for small results in the zero-to-one stage. Early new businesses have small revenue and high uncertainty. Without a structure to endure that, the sprout disappears before it grows.
### How should startups compete with large companies?
At first, solve a narrow and sharp problem rather than fighting broadly. Build customer loyalty in a niche that large companies pay less attention to, then expand.
### What matters most when reducing bureaucracy?
Return decision-making to customers and the field, and build a reward system in which people who execute innovation are actually recognized.
### How should companies balance systems and autonomy?
It depends on industry risk and customer touchpoints. Safety-critical industries need more system, while industries that need fast experiments can allow more mission-based autonomy.
[Original Korean article](https://www.thinknote.co.kr/ai-era-business-innovation-system-mission/)